Generally described, computing devices may utilize machine learning models to process visual information. Machine learning models may be trained to recognize and/or classify images by providing a set of known inputs (e.g., sample images and other data) and desired outputs. The model may thereby be trained to formulate general rules for classifying images, and may classify new images or provide other outputs according to its learned rules. Machine learning models may also be trained using techniques such as reinforced learning (e.g., providing positive or negative feedback in response to an output), or may be used without output-based training to identify unknown patterns in the input data, such as a previously unknown relationship between the inputs. The results (outputs) produced by a machine learning model may thus vary according to the inputs and the techniques used to train the model, as well as the characteristics of the machine learning model itself.
A user of computer vision machine learning models may thus be faced with a multitude of models, each of which may be more or less effective when used in particular applications or with particular inputs, and each of which may require different inputs and provide different outputs.